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Stability and Convergence of Stochastic Particle Flow Filters
In this paper, we examine dynamic properties of particle flows for a recently derived parameterized family of stochastic particle flow filters for nonlinear filtering and Bayesian inference.
A New Parameterized Family of Stochastic Particle Flow Filters
We prove that the particle flows are unbiased under the assumption of linear measurement and Gaussian distributions, and that estimates constructed from the stochastic flows are consistent.
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